The multi-scale convolutional neural networks-based deep learning model,
called MulCNN-HSP, is proposed for the classification of heat shock proteins.
Given a protein sequence, it is first encoded using one-hot encoding and then fed into the multi-scale convolution.
After the convolution operation, we incorporate global max pooling and dropout layers.
Ultimately, the input protein sequence will be classified through a fully connected layer.
